Matching Offers to Known Products

ABSTRACT

A method and apparatus for electronically matching an electronic offer to structured data for a product offering is disclosed. The structure data is reviewed and a dictionary of terms for each attribute from the structure data is created. Attributes in unstructured text may be determined. Each pair of the attributes (name and value) from the unstructured data and the structured data are obtained, the attribute pairs of the structured data and the unstructured data and compared and a similarity level is calculated for the matching the attribute pairs. The structured data pair that has the highest similarity score to the unstructured data pair is selected and returned.

BACKGROUND

This Background is intended to provide the basic context of this patentapplication and it is not intended to describe a specific problem to besolved.

Vendors usually desire to have their good available and easily locatedon the Internet. However, submitting offers to aggregator and searchsites is not a simple task. The sites usually require the data to besubmitted as structured data and creating the structured data file maybe a challenge. At the same time, the aggregator and search sites have asignificant amount of offer data already stored as structure data.Trying to match simple vendor descriptions to stored structured data hasbeen a challenge.

Some web sites manually identify attributes to match on for eachcategory, and define a matching between offer and product when thevalues for all these attributes agree. There are two main limitations ofthis approach: first, for every category, the vendor needs to manuallyidentify attributes that are important to be matched on. Second, thematcher may have to rely on extracting all the important attributescorrectly from the unstructured description of offers, and thus cannothandle missing attributes. In addition, since there is no notion ofrelative importance between attributes, any mismatched attribute (e.g.Color vs. brand) gets the same penalty.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

A method and apparatus for electronically matching an electronic offerto structured data for a product offering is disclosed. The structuredata is reviewed and a dictionary of terms for each attribute from thestructure data is created. Attributes in unstructured text may bedetermined. The unstructured text may be submitted to a parsing systemwhere the parsing system parses the unstructured text. The parsing mayoccur in variety of ways. In some embodiments, each pair of theattributes (name and value) from the unstructured data and thestructured data are obtained, the attribute pairs of the structured dataand the unstructured data and compared and a similarity level iscalculated for the matching the attribute pairs. The structured datapair that has the highest similarity score to the unstructured data pairis selected and returned.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a computing device;

FIG. 2 is an illustration of a method and apparatus for electronicallymatching an electronic offer to structured data for a product offering;

FIG. 3 is an illustration of structured data;

FIG. 4 a-4 c are illustrations of unstructured data offers; and

FIG. 5 a-5 b are illustrations of parsings of unstructured data offers.

SPECIFICATION

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent. The detailed description is to be construedas exemplary only and does not describe every possible embodiment sincedescribing every possible embodiment would be impractical, if notimpossible. Numerous alternative embodiments could be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

It should also be understood that, unless a term is expressly defined inthis patent using the sentence “As used herein, the term ‘______’ ishereby defined to mean . . . ” or a similar sentence, there is no intentto limit the meaning of that term, either expressly or by implication,beyond its plain or ordinary meaning, and such term should not beinterpreted to be limited in scope based on any statement made in anysection of this patent (other than the language of the claims). To theextent that any term recited in the claims at the end of this patent isreferred to in this patent in a manner consistent with a single meaning,that is done for sake of clarity only so as to not confuse the reader,and it is not intended that such claim term be limited, by implicationor otherwise, to that single meaning. Finally, unless a claim element isdefined by reciting the word “means” and a function without the recitalof any structure, it is not intended that the scope of any claim elementbe interpreted based on the application of 35 U.S.C. §112, sixthparagraph.

FIG. 1 illustrates an example of a suitable computing system environment100 that may operate to execute the many embodiments of a method andsystem described by this specification. It should be noted that thecomputing system environment 100 is only one example of a suitablecomputing environment and is not intended to suggest any limitation asto the scope of use or functionality of the method and apparatus of theclaims. Neither should the computing environment 100 be interpreted ashaving any dependency or requirement relating to any one component orcombination of components illustrated in the exemplary operatingenvironment 100.

With reference to FIG. 1, an exemplary system for implementing theblocks of the claimed method and apparatus includes a general purposecomputing device in the form of a computer 110. Components of computer110 may include, but are not limited to, a processing unit 120, a systemmemory 130, and a system bus 121 that couples various system componentsincluding the system memory to the processing unit 120.

The computer 110 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer180, via a local area network (LAN) 171 and/or a wide area network (WAN)173 via a modem 172 or other network interface 170.

Computer 110 typically includes a variety of computer readable mediathat may be any available media that may be accessed by computer 110 andincludes both volatile and nonvolatile media, removable andnon-removable media. The system memory 130 includes computer storagemedia in the form of volatile and/or nonvolatile memory such as readonly memory (ROM) 131 and random access memory (RAM) 132. The ROM mayinclude a basic input/output system 133 (BIOS). RAM 132 typicallycontains data and/or program modules that include operating system 134,application programs 135, other program modules 136, and program data137. The computer 110 may also include other removable/non-removable,volatile/nonvolatile computer storage media such as a hard disk drive141 a magnetic disk drive 151 that reads from or writes to a magneticdisk 152, and an optical disk drive 155 that reads from or writes to anoptical disk 156. The hard disk drive 141, 151, and 155 may interfacewith system bus 121 via interfaces 140, 150.

A user may enter commands and information into the computer 110 throughinput devices such as a keyboard 162 and pointing device 161, commonlyreferred to as a mouse, trackball or touch pad. Other input devices (notillustrated) may include a microphone, joystick, game pad, satellitedish, scanner, or the like. These and other input devices are oftenconnected to the processing unit 120 through a user input interface 160that is coupled to the system bus, but may be connected by otherinterface and bus structures, such as a parallel port, game port or auniversal serial bus (USB). A monitor 191 or other type of displaydevice may also be connected to the system bus 121 via an interface,such as a video interface 190. In addition to the monitor, computers mayalso include other peripheral output devices such as speakers 197 andprinter 196, which may be connected through an output peripheralinterface 195.

FIG. 2 is an illustration of a method of receiving unstructured data 400from a vendor and matching it with stored structure data 300. Structureddata 300 may be data that is stored in a database that already hasattributes and values. FIG. 3 may be an illustration of structured data300. Unstructured data 400 may be data that comes from a vendor thatdescribes a product for sale but is not in a structured formant. FIGS. 4a, 4 b and 4 c may be illustrations of unstructured data 400. In thisspecific example, FIG. 3 may illustrate a structured record orstructured data 300 for Panasonic DMC-FX07 digital camera in the productcatalog and FIGS. 4 a-4 c may be three merchant offers for this product.The structured record 300 may be created from the product specificationsprovided by a reputed supplier of information on consumer electronicgoods and may be added to the category Digital camera by a classifierused for this purpose.

While Offer-1 (FIG. 4 a) may be the most detailed, it still may containonly a small part of the information in the structured record 300. Thephrase ‘Panasonic Lumix’ indicates both Brand (Panasonic) as well asProduct Line (Panasonic Lumix). Some of the attribute values 305 onlymatch approximately (7.2 megapixel vs. 7 Megapixel, LCD Monitor vs. LCDdisplay). The only attribute name 305 present in the offer is OpticalZoom (it is called Lens System/Optical Zoom in the structuredrecord/data 300). The corresponding values for this attribute 305 may be3.6×vs. 3.6.

Information provided in Offer-2 (FIG. 4 b) may be largely a subset ofwhat is provided in Offer-1. This offer provides the values of Categoryand Brand but the value of the Model has an extra suffix though, itadditionally provides the value of the Color attribute. Offer-3 (FIG. 4c) provides part of the value of the product line (Lumix) and somewhatdifferent value for Sensor Resolution (7.2 MP vs. 7 Megapixel) as wellas Model (FX07EB-S vs. DMC-FX07). It neither provides Category nor Brandinformation. With respect to Offer-3, note further that Panasonic alsomakes other 7.2 megapixel Lumix digital cameras (e.g. DMC-TZ3K, DMC-LZ6,and DMC-FX12). Moreover, FX07 is also a model number for a Field.

Controller Product.

One possible approach automatically develops semantic understanding ofthe unstructured text by leveraging structured information in thedatabase, and learns a function for matching previously unseenunstructured text 400 to structured database records 300. This matchingfunction may be defined over a set of automatically selected attributes.It may evaluate the similarity between the semantic inference ofunstructured texts and entities, while taking into account both therelative importance of the attributes and the difference between missingvalues and mismatched values. Only a small number of matched examplesmay be needed for learning the function.

Training Methodology

Accordingly, at block 200, the structure data may be reviewed. Say thereis a database S of entities, represented as structured record/data 300such as the structured record/data 300 illustrated in FIG. 3. Everystructured record/data 300 sεS may consist of a set of attribute305<name, value>pairs. An unstructured text u may be received as input,which may be a concise free-text description that specifies values for asubset of the attributes in S in an arbitrary manner such as theillustrations in FIGS. 4 a-4 c. The values in the text may not preciselymatch those found in the database of structured offers and it may alsocontain additional words. An objective may be to match u to one or morestructured records 300 in S. The metric of precision and recall may beused for judging the quality of the matching system.

In order to train the matching system, a set U of unstructured textualdescriptions may be postulated. The size M of S (correctly matchedstructured records 300) may be much larger than the size N of U(mismatched unstructured offers 400). Each uεU may be assumed to havebeen matched to one structured record 300 in S. Similarly, N mismatchedrecords from S may be assumed to be available, one for every uεU. Theset U may already exist. For example, in an e-commerce catalog scenario,the previously matched offers can yield U. These matches can be obtainedwhen universal codes such as Global Trade Item Numbers (GTINs) areavailable for both offers and products, otherwise, some labeling effortmay need to be expended.

A probabilistic approach may be taken to find the structured record/data300 sεS that has the largest probability of matching to the givenunstructured text u, a probability that may be denoted by P(match(u;s)). A method for matching may be used that is based on understandingthe semantics of the textual features in U, and relating them to thestructured attributes in S. This may allow attribute-specificsimilarities to be measured, and to combine them in a way that mayaccount for the fact that not all attributes are equally important formatching, and that mismatching on important attributes may carry higherpenalty for a match than missing an attribute. It is often may be thecase that a categorization is defined over the structured records 300 inS. In such cases, use a classifier may be built using well knowntechniques to categorize the unstructured text and localize the matchingto the structured records 300 within that category.

Algorithms

One embodiment of a matching system may have an offline stage, where thestructured records 300 may be preprocessed and a machine learningalgorithm may be trained that learns to match pairs of structured 300and unstructured records 400, and an on-line stage where given, a newunstructured record, the best matched structured record(s) 300 may befound using the trained model. In the offline stage, the system may beinterested in learning how to match using training data in the form ofmatched and mismatched pairs of unstructured and structured records 300.The system may discover the subset of attributes that are most importantfor matching, and their relative importance. For this, the system mayneed to know the attributes present in unstructured text.

The following may be a sample algorithm for training the method. Thetraining may occur in advance of receiving the offer from the vendor andmay be completed offline or at a different location.

Algorithm 1 Off-line Training   Input: U = {u₁ . . . u_(N) } - a set ofunstructured records S = {s₁ . . . s_(M)} - a set of structured records,M >> N M = { 

u_(i), s_(j) 

 _(i)}_(i=1) ^(N), (u_(i) ε U, s_(j) ε S) - pairs of correctly matchedrecords, one for every u_(i). N = { 

u_(i), s_(k) 

 _(i)}_(i=1) ^(N) - similarly, pairs of mismatched records. Output: D -dictionaries, K - list of key attributes w - algorithm parametersPreprocess: D 

 CreateAttributeDictionaries(S) - Construct normal- ized attributedictionaries (Sec. 2.2) Train: for all u ε U do  û 

 SemanticParsing(u, D) - Extract putative at-  tributes using semanticpassings (Sec. 2.3) end for K 

 IdentifyKeyAttributes({û}) - (Sec. 2.4) for all pairs ε M and pairs ε Ndo  f_(i) ^(M) 

 ExtractSimFeaturs(pair_(i), K)  f_(j) ^(N) 

 ExtractSimFeaturs(pair_(j), K) - Construct simi-  larity feature vectorfor matched and mismatched pairs  (Sec. 2.5) end for w 

 arg max_(ω) LearnToMatch(F(ω, f), {f_(i) ^(M)}, f_(j) ^(N)}, ω) - Traina function that maps feature vectors to match prob- ability, F(ω, f) : f→ [0, 1] (Sec. 2.6) Return: w. K, D

An additional part of the method may also be performed online or in realtime when the offer of unstructured data 400 is received from thevendor. The following is one embodiment of a method that may be executedwhen an offer of unstructured data 400 is received from a vendor.

Algorithm 2 Online Matching   Input: u - an unstructured record S, D, K,w Output: s* - best matching s ε S û 

 SemanticParsing(u, D) - (Sec. 2.:3) for all s_(i) ε S do  f_(i) 

 ExtractSimFeaturs( 

û, s_(i) 

, K) - (Sec. 2.5)  P(match(s_(i), u)) 

 F(w, f_(i)) - Matching score of a pair  (Sec. 2.7) end for Return: s* =arg max_(s) _(i) P(match(u, s_(i))) - Best Matching score of all pairs(Sec. 2.7)

To this end, the system may use an approach for flexible semanticparsing that may leverage the dictionaries from the structured records300. The system, then, may use the parses to identify the set of keyattributes present in unstructured texts, based on their probability ofoccurrence. Then, for every training data pair of unstructured andstructured record/data 300, the system may construct a similarityfeature vector representation that measures similarity for every keyattribute. The system may use these similarity feature vectors todiscover the relative importance of key attributes for matching bylearning a scoring function designed to assign high scores tounstructured 400 and structured record-pairs 300 that are correctlymatched, and low scores to record-pairs that are mismatched. In theonline stage, the system may be given a previously unseen unstructuredtext and the system may find its best matched structured record/data 300by applying the semantic parsing module on the input text for the set ofkey attributes discovered in the offline stage. A similarity featurevector may be extracted for the unstructured record 400 with eachstructured record 300 and its matching score may be computed using thescoring function learned in the offline stage, choosing the highestscoring structured record 300 as the best match.

Attribute Dictionaries

At block 205, given the database of structured records 300 S,attribute-specific dictionaries of values may be constructed by poolingin, for each attribute, the values it takes across all records. Duringthis construction, standard preprocessing steps may be employed such asunit conversion and name synonymization to ensure that values arebrought to a canonical form.

Directional synonyms may also be used. The value of an attribute 305 maybe defined to be the directional synonym of another when theirequivalence relationship is directional, i.e., when a value has theproperty of being a subset of another value for some attribute. As anexample, consider the category of hard drive products that has anattribute 305 that corresponds to the hard drive interface type. Valuesof interface types include ‘SATA’, ‘ATA’ and ‘SCSI’. An interface typevalue can have other values as its subtypes that allow the main value tobe a synonym of its subtype value, and not vice versa. For example,‘SATA-200’ and ‘SATA-100’ are subtypes of ‘SATA’. Thus, ‘SATA-100’ canbe replaced by ‘SATA’ but not vice versa. In general, information aboutthe directionality of synonyms may be maintained when evaluating theequivalence of values in the matching stage.

Semantic Parsing

At block 210, attributes in unstructured text may be determined. Theunstructured text may be submitted to a parsing system wherein theparsing system parses the unstructured text. Unstructured records 400may be segmented into possibly overlapping regions such that some ofthese regions are associated with one or more attributes. For this,standard tokenization of the unstructured text may be used, and then usethe attribute dictionaries from the previous step may be used toassociate potential attributes to a subset of the tokens. Theassociation between tokens in the unstructured text to one or moreattribute 305 names may be referred to as semantic parsing. A keyfeature of our parsing is that the association may be highly flexible sothat no hard decision is made until the matching step. Instead ofcommitting to a fixed parsing, all possible and potentially overlappinglabels for subsets of tokens may be maintained in the unstructuredrecord 400 as unstructured records 400 may be too concise and containinsufficient context information to resolve ambiguities. Making harddecisions prematurely may undermine successful matching.

As an example, in FIG. 5( a), the segmented tokens and their respectivelabeling may be shown for an unstructured text description of a digitalcamera. ‘Panasonic’ may be seen as both a Brand and part of ProductLine, while both ‘Lumix’ and ‘Panasonic Lumix’ have been labeled asvalid Product Lines.

Identifying Key attributes

In standard structured to structured record 300 matching where thevalues of all attributes are provided, it is typically assumed that theset of attributes for each record is the same. In the presentformulation, this is not the case, and therefore, akin to featureselection, a small set of attributes may be selected that are importantfor matching and the small set may be called a subset of attributes orkey attributes.

Referring again to FIG. 2, at block 215 the text of the unstructureddata 400 may be reviewed and at block 220, statistical data regardingattribute 305 frequency in the unstructured data 400 may be collected.Of course other statistics may be determined from the data and used toestablish the attributes.

At block 225, the key attributes may be selected from the unstructuredtext. In one embodiment, at block 230, the probability an attribute 305is a key attribute 305 may be determined by studying structured data300, for example. At block 230, if the probability is above a threshold,marking the attribute 305 as one of the key attributes and at block 235,if the probability is below a threshold, not marking the attribute 305as one of the key attributes.

Note that it is not expect every unstructured record 400 to have valuesfor all key attributes, but for each to have values for some of them.Hence, after the semantic parsing step, statistics about attributesfrequently present in the unstructured data 400 may be collected, andthe key attributes may be selected to be the set of attributes K suchthat each attribute 305 kεK satisfies:

$\begin{matrix}{{\frac{\sum\limits_{u \in U}{I\left\lbrack {{u \cdot {{val}(k)}} \in \mspace{11mu} {{values}(k)}} \right\rbrack}}{U} \geq {\eta \mspace{14mu} {\forall{k \in \kappa}}}},} & {{Equation}\mspace{14mu} 1}\end{matrix}$

where u:val(k) represent the value of the attribute 305 k for u,[u:val(k)εvalues(k)]:=1 if u:val(k) is an valid value of attribute 305k, and I[z] is an indicator function that evaluates to 1 if z=1 or elseto zero. u:val(k) represents the values for the attribute k found in u,[u.val(k)≠0|=TRUE if at least one valid value for attribute k was foundin string u, and I[z] is an indicator function that evaluates to 1 ifz=TRUE or else to zero. In one experiment, n was picked to =0.6indicating that each key attribute 305 should be present in 60% of U.

Extracting Similarity Features Between Unstructured and StructuredRecord Pairs

At this stage, the method may have all possible semantic parsings of theunstructured text records. At block 240, the method may obtain theattribute 305 level similarity between some unstructured record 400 uand structured record 300 s for the key attributes K identified in theprevious step. For this, at block 250, the method may obtain for eachpair (u, s) the parsing of u (which may be denote by û) (unstructureddata 400) that enables obtaining the best agreement between theattribute 305 [name, value] pairs of s (structured data 300) withattribute 305 [name, value] pairs of û. Such a parsing may be referredto as the maximal parsing of u corresponding to s.

Consider FIG. 5( a) which shows an unstructured record 400 that has beenparsed in multiple ways during the semantic parsing step. In particular,attribute 305 Product Line has two possible values, ‘Lumix’ and‘Panasonic Lumix’. In this example, the maximal parsing of ucorresponding to product s1 may have only ‘Panasonic Lumix’ as theproduct line, and ‘Lumix’ as Product Line will be discarded. For s2, asit does not have the Product Line specified (missing), either values canbe chosen. Given the maximal parsing for a pair [û, s], a similarityfeature vector f of length |K| may be created, and its elements may bepopulated with similarity levels between the maximally-parsed and s forthe corresponding key attribute. Here, similarity may be measured basedon the type of attributes:

Binary: Attributes whose values need to be matched exactly. Examples ofsuch attributes include brand, product line, etc.

Numeric: Attributes whose values measure numeric quantities. Often thesevalues are fuzzy either because of round-off errors (e.g. 7 MP vs. 7.2MP) or slightly variant conversion factors (1 GB=1000 MB or 1 GB=1024MB). Examples of such attributes include capacity, weight etc.

Missing vs. mismatched attribute values

It is often the case that the method may not be able to infer values forsome key attribute 305 k_(i). If the method were to indicate this by avalue of 0 in the ith position of the similarity feature vector, f, themethod may be treating missing values in the same way as mismatchedvalues. However, the method should not penalize a matching score whereeither or both of û and s are missing a specific attribute 305 in thesame way the method may penalize the score when they disagree on thatattribute. In fact, if there is a disagreement on an attribute 305value, it is a stronger indicator of a mismatch of the pair than if theattribute 305 value is simply missing from either one.

For instance, say that the key attributes for matching on digitalcameras are brand, model, product line, optical sensor resolution anddisplay diagonal size. FIG. 5A may show an example of unstructureddigital camera record u that has been semantically parsed, and FIG. 5Bpresents two structured records 300 s1 and s2 from the same category,and the maximal parsing of u with respect to these records. Of the fivekey attributes for cameras, u has agreement with s1 and with s2 on threeattributes. While s2 is missing two key attributes (product line andoptical sensor resolution), s1 has a mismatch on model and is missingthe optical sensor resolution. Thus, s1 and s2 are different in terms ofthe attributes they agree on, mismatch on, and are missing with respectto u. This difference between the pairs [u; s1] and [u; s2] should becaptured so that during the matching stage, it can be used to score thematches appropriately. This difference between mismatching and missingbecomes even more important when the corresponding attributes havediffering strength of importance.

At block 255, a similarity level may be calculated for the matching theattribute 305 pairs of the structured data 300 and the unstructured data400. In one embodiment, the method may therefore define similarity in away that captures this difference. Let u:val(k) and s:val(k) representthe value of some attribute 305 k for u and s, respectively. Thesimilarity between u and s for attribute 305 k may be defined to be:

$\begin{matrix}{f_{k} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu} {u \cdot {{val}(k)}}} = {\varnothing \mspace{14mu} {OR}}} & {{s \cdot {{val}(k)}} = \varnothing} \\\left( {- 1} \right) & {I\left\lbrack {{{{u \cdot {{val}(k)}} - {s \cdot {{val}(k)}}}} > \lambda} \right\rbrack} & {otherwise}\end{matrix} \right.} & {{Equation}\mspace{14mu} 2}\end{matrix}$

where I[z] is the indicator function, and λ=0 for binary attribute. Insome embodiments, the method may used λ=1 for numeric attributes. Notethat the feature similarity becomes 0 when a value is missing, and maybe set to −1 when the values are mismatched. Of course, other manners ofdetermining similarity may be possible and are contemplated.

Learning Relative Importance

Instead of treating each key attribute 305 as equally important, themethod may learn their relative importance between them using a binarylogistic regression of the form:

$\begin{matrix}\begin{matrix}{{\mathcal{F}\left( {w,f} \right)} = {P\left( {{y = {1f}},w} \right)}} \\{= \frac{1}{1 + {\exp \left\{ {- \left( {b + {f^{T}w}} \right)} \right\}}}}\end{matrix} & {{Equation}\mspace{14mu} 3}\end{matrix}$

The logistic regression learns a mapping from the similarity featurevector f to a binary label y, through the logistic function. Theparameter w may be the weight vector wherein each component W_(k)measures the relative importance of the corresponding feature f_(k) forpredicting the label y. The method may have on hand all matched andmismatched training pairs, and let

f _(i) =[b _(i1) ; f _(i2) , : : : , f _(i|K|)]

be the feature vector for pair i. Now, let

{F;Y}={(f ₁ ,y ₁), . . . , (f _(N) ;Y _(N))}

be the set of feature vectors along with their corresponding binarylabels. Here, y_(i)=1 indicates that the ith pair is a match, otherwisey_(i)=0. Logistic regression maximizes an objective function which isthe conditional log-likelihood of the training data P(Y|F,w):

$\begin{matrix}{{\underset{w}{\arg \; \max}\; \log \; {P\left( {{YF},w} \right)}} = {\underset{w}{\arg \; \max}{\sum\limits_{i = 1}^{N}{\log \; {P\left( {{y_{i}f_{i}},w} \right)}}}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

where P(y_(i)=1|f_(i), w) is defined by Eq. 3. Note that a feature withpositive weight may affect the score by increasing the probability ofmatch for a pair with agreement on the feature, by decreasing the scorein the case of a mismatch, and by leaving the score unaffected in thecase of a missing value. While Eq. 4 is a convex function with a globaloptimal solution, the form of the function does not lend itself to ananalytical solution, and therefore the method may use the techniqueproposed in [3].

Matching

During the online phase, the method is given an unstructured text uεU,and the goal is to identify its best matching structured record 300 sεS.The scoring function learned during the offline phase (Eq. 3) mayprovide the probability of match for a pair [u, s]. Hence, the methodmay find the best match by pairing u with every sεS, calculating theirmatch score, and picking the s* that results in the highest score. Forpractical implementation, instead of actually pairing u with every sεS,the method leverages on efficient inverted indices on the attribute 305values to obtain a putative (usually small) subset of S that are likelyto be a match. Then, the method finds the best match within this subsetusing the method described in the previous paragraph. Efficient indexingschemes (in the context of record linkage) may also be used andexploited in our framework. At block 260, the structure data that hasthe highest similarity score to the unstructured data 400 pair may beselected and at block 265, the structured data 300 pair that has thehighest similarity score to the unstructured data 400 pair may bereturned.

Exploiting Domain Knowledge

Often, it may be the case that the method may have domain knowledgeabout certain attributes that can be leveraged to learn a bettermatching function. In this section, an example of such an attribute 305is presented that is common in e-commerce settings. In particular, alarge number of commercial products have the model attribute. However,models are specified in un-structured text in varied ways which would bedifficult to learn using reasonably sized training data. A scoringfunction is designed that captures the nuances of model variations basedon the following:

1. Most models exhibit a left-to-right order of importance of thecharacters in their strings.

2. Some models begin with a standard prefix that is associated with aparticular manufacturer. For example, all Panasonic digital camerasmodels start with ‘DMC’. Many merchants may not provide this prefix intheir unstructured textual description of products and therefore absenceof such prefixes need not be penalized as much as, say, missing themodel number.

3. Mismatching or missing numbers is a stronger indication of a mismatchthan mismatching or missing post-fix letters. For instance ‘DMC-FX07-K’and ‘DMC-FX07-S’ are likely different colors (less important variation)of the same model, whereas ‘DMC-FX05’ and ‘DMC-FX07’ are likely twodifferent models. Similarly, ‘DMC-FX2EB’ is most likely a type of‘DMC-FX07’ while ‘DMC-FX15’ is a model different from ‘DMC-FX150’.Keeping these properties of how a model number is specified in mind, themethod may define a scoring function score_(model) that assignssimilarity in the range [0,1] between two models u.val(model) ands.val(model). The function may be inspired by the edit-distance scorebut in addition has facility for controlling the importance of missingcharacters as opposed to mismatched ones, and discriminating betweenmissing prefix and postfix penalties:

$\begin{matrix}{{score}_{model} = \frac{x}{x + {\alpha \; y} + {\beta \; z} + {\gamma \; t}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

Here, x is the size of the string consisting of consecutively matchedcharacters. Next, y is the number of missing prefix characters, z is thenumber of missing postfix characters and t is the number of mismatchedcharacters such that x+y+z+t=|u:val(model)|, the length of u:val(model).The model first computes best local alignment between u:val(model) ands:val(model). If s:val(model) is shorter than u:val(model), theunaccounted for characters in u:val(model) are considered missing.

The method may wish to differentiate between mismatches beginning with amismatched letter and mismatches beginning with a mismatched number, ormissing letters and numbers and hence use specific α₁, α₂, β₁, β₂, γ₁,γ₂ where the subscript 1 is for the number case, and 2 for the lettercase. In this work, a tuning set may be used to learn {α₁, α₂, β₁, γ₁,γ₂}:={1:5; 0:5; 1; 8; 3} for our application. The method may alsoperform sensitivity analysis to ensure that the performance is notsensitive to these values. From the parameter setting, it may be seenthat missing characters in a model carry less penalty than mismatchedcharacters (γ_(i)>β_(i)>α_(j), ∀i, j).

Performance Metric

For evaluation purpose, a set of unstructured offers uεU may beprovided. Assume that an oracle can provide, for each u, correctlymatched structured product S_(u). The matcher will have no knowledgeabout S_(u), but instead predict the best matched product S*_(u) withprobabilistic score η_(u,s)*_(u), as defined by Eq. 3. Note that by bestmatched, the method may determine that there is no other s that canmatch u with a higher score. Thus, the method may be evaluatingperformance on the harder task of the matcher finding the best matchingproduct for every unstructured offer u.

Define θε[0, 1] to be the threshold on the probability output by thescoring function defined in Equation. 3. Precision and recall atthreshold level θ may be defined as:

$\begin{matrix}{{{Precision}(\theta)} = \frac{\left. {\sum\limits_{u \in U}{{I\left\lbrack {\eta_{u,s_{u}^{*}} > \theta} \right\rbrack}\mspace{14mu} {AND}\mspace{14mu} {I\left\lbrack {s_{u}^{*} = s_{u}} \right)}}} \right\rbrack}{\sum\limits_{u \in U}{I\left\lbrack {\eta_{u,s_{u}^{*}} > \theta} \right\rbrack}}} & {{Equation}\mspace{14mu} 6} \\{{{Recall}(\theta)} = \frac{\sum\limits_{u \in U}{{I\left\lbrack {\eta_{u,s_{u}^{*}} > \theta} \right\rbrack}\mspace{14mu} {{AND}\mspace{14mu}\left\lbrack {s_{u}^{*} = s_{u}} \right\rbrack}}}{C}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

where I[z] is the indicator function with values (0, 1).

Data set for training and testing

As another example, the method considered three categories from anonline catalog: Digital cameras, LCD TVs and Ovens. These categorieswere selected as the products in these categories have different typesof attributes and the characteristics of the offers are also different.In the Digital camera category, there were 3,019 products and 2,845offers while these numbers for LCD TVs and Ovens were (4,574 and 1,386)and (6,852 and 3,954), respectively. The offers were hand-matched to thecorresponding products providing the method with a truth set forevaluation.

Training set: For each category, the method may sample 10% of the offersalong with their matchings to the products to be used as training set.This act as the positive (matched) examples for training. A set ofnegative examples may also be created by pairing products that are notmatched with offers.

Test set: The remaining 90% of the matched pairs may be used forcomputing performance metrics. At test time, the matcher without anyknowledge of the true match, returns the most confidently matchedstructured record 300 for each offer u. This is compared against theknown true match to evaluate precision and recall.

In conclusion, the detailed description is to be construed as exemplaryonly and does not describe every possible embodiment since describingevery possible embodiment would be impractical, if not impossible.Numerous alternative embodiments could be implemented, using eithercurrent technology or technology developed after the filing date of thispatent, which would still fall within the scope of the claims.

1. A computer based method of electronically matching an electronicoffer to structured data comprising a product offering comprising:reviewing the structured data; creating a dictionary of terms for eachattribute from the structured data; determining attributes inunstructured text comprising submitting the unstructured text to aparsing system wherein the parsing system parses the unstructured textcomprising: reviewing the text of the unstructured data; collectingstatistical data regarding attribute frequency in the unstructured data;selecting key attributes from the unstructured text comprisingdetermining the probability an attribute is a key attribute by studyingstructured data; if the probability is above a threshold, marking theattribute as one of the key attributes; if the probability is below athreshold, not marking the attribute as one of the key attributes;obtaining attribute level similarity between the unstructured record andstructured records for the key attributes comprising; maximal parsingthe unstructured record comprising for each of the attributes abovethreshold  obtain each pair of the attribute from the unstructured dataand the structured data;  matching the attribute pairs comprising a nameand a value of the structured data and the unstructured data; calculating a similarity level for the matching the attribute pairscomprising a name and a value of the structured data and theunstructured data  selecting the structured data pair that has thehighest similarity score to the unstructured data pair; and returningthe structured data pair that has the highest similarity score to theunstructured data pair.
 2. The method of claim 1, wherein calculating asimilarity level further comprising creating a similarity feature vectorf length K and populate its elements with similarity levels between themaximum unstructured record and s for the corresponding K (keyattribute) based on attribute type to enable the maximum agreementbetween the attribute pairs of structured data and the attribute pairsof unstructured data.
 3. The method of claim 1, wherein if the attributetype is binary, requiring an exact match and if the attribute type isnumeric, requiring a best match.
 4. The method of claim 1, furthercomprising training the system with unstructured and structured pairswhich are known to be matched and known to be mismatched.
 5. The methodof claim 1, further comprising segmenting the unstructured records intoregions wherein the regions may overlap.
 6. The method of claim 1,wherein segmenting the unstructured records into regions wherein theregions may overlap further comprises: adding tokens to the unstructuredtext; and using the dictionary to associate potential attributes to asubset of the tokens.
 7. The method of claim 6, wherein the dictionaryis directional.
 8. The method of claim 1, wherein matches with longercharacter match runs between unstructured data structured data valuesand are given more weight than attribute matches with shorter runmatches between unstructured data structured data values.
 9. A computingsystem for electronically matching an electronic offer to structureddata comprising a product offering comprising a processor physicallyconfigured to execute computer executable instructions, a memory forstoring the computer executable instructions and an input/output device,the computer executable instructions comprising instructions for:reviewing the structured data; creating a dictionary of terms for eachattribute from the structured data; determining attributes inunstructured text comprising submitting the unstructured text to aparsing system wherein the parsing system parses the unstructured textcomprising: reviewing the text of the unstructured data; collectingstatistical data regarding attribute frequency in the unstructured data;selecting key attributes from the unstructured text comprisingdetermining the probability an attribute is a key attribute by studyingstructured data; if the probability is above a threshold, marking theattribute as one of the key attributes; if the probability is below athreshold, not marking the attribute as one of the key attributes;obtaining attribute level similarity between the unstructured record andstructured records for the key attributes comprising; maximal parsingthe unstructured record comprising for each of the attributes abovethreshold  obtain each pair of the attribute from the unstructured dataand the structured data;  matching the attribute pairs comprising a nameand a value of the structured data and the unstructured data; calculating a similarity level for the matching the attribute pairscomprising a name and a value of the structured data and theunstructured data  selecting the structured data pair that has thehighest similarity score to the unstructured data pair; and returningthe structured data pair that has the highest similarity score to theunstructured data pair.
 10. The computing system of claim 9, wherein thecomputer executable instructions for calculating a similarity levelfurther comprising computer executable instructions for creating asimilarity feature vector f length K and populate its elements withsimilarity levels between the maximum unstructured record and s for thecorresponding K (key attribute) based on attribute type to enable themaximum agreement between the attribute pairs of structured data and theattribute pairs of unstructured data.
 11. The computing system of claim9, wherein if the attribute type is binary, requiring an exact match andif the attribute type is numeric, requiring a best match.
 12. Thecomputing system of claim 9, further comprising computer executableinstructions for training the system with unstructured and structuredpairs which are known to be matched and known to be mismatched.
 13. Thecomputing system of claim 9, further comprising computer executableinstructions for segmenting the unstructured records into regionswherein the regions may overlap.
 14. The computing system of claim 13,wherein segmenting the unstructured records into regions wherein theregions may overlap further comprises: adding tokens to the unstructuredtext; and using the dictionary to associate potential attributes to asubset of the tokens.
 15. The computing system of claim 9, wherein thedictionary is directional.
 16. The computing system of claim 9, whereinmatches with longer character match runs between unstructured datastructured data values and are given more weight than attribute matcheswith shorter run matches between unstructured data structured datavalues.
 17. A computer storage medium physically configured according tocomputer executable instructions for electronically matching anelectronic offer to structured data comprising a product offering, thecomputer executable instructions comprising instructions for: reviewingthe structured data; creating a dictionary of terms for each attributefrom the structured data; determining attributes in unstructured textcomprising submitting the unstructured text to a parsing system whereinthe parsing system parses the unstructured text comprising: reviewingthe text of the unstructured data; collecting statistical data regardingattribute frequency in the unstructured data; selecting key attributesfrom the unstructured text comprising determining the probability anattribute is a key attribute by studying structured data; if theprobability is above a threshold, marking the attribute as one of thekey attributes; if the probability is below a threshold, not marking theattribute as one of the key attributes; obtaining attribute levelsimilarity between the unstructured record and structured records forthe key attributes comprising; maximal parsing the unstructured recordcomprising for each of the attributes above threshold  obtain each pairof the attribute from the unstructured data and the structured data; matching the attribute pairs comprising a name and a value of thestructured data and the unstructured data;  calculating a similaritylevel for the matching the attribute pairs comprising a name and a valueof the structured data and the unstructured data  selecting thestructured data pair that has the highest similarity score to theunstructured data pair; and returning the structured data pair that hasthe highest similarity score to the unstructured data pair.
 18. Thecomputer storage medium of claim 17, wherein the computer executableinstructions for calculating a similarity level further comprisingcomputer executable instructions for creating a similarity featurevector f length K and populate its elements with similarity levelsbetween the maximum unstructured record and s for the corresponding K(key attribute) based on attribute type to enable the maximum agreementbetween the attribute pairs of structured data and the attribute pairsof unstructured data.
 19. The computer storage medium of claim 17,wherein if the attribute type is binary, requiring an exact match and ifthe attribute type is numeric, requiring a best match.
 20. The computerstorage medium of claim 17, further comprising computer executableinstructions for training the system with unstructured and structuredpairs which are known to be matched and known to be mismatched andwherein matches with longer character match runs between unstructureddata structured data values and are given more weight than attributematches with shorter run matches between unstructured data structureddata values.